Image object segmentation processes digital images containing objects of interest and determines the regions in the images corresponding to those objects of interest. Image object segmentation is critical for many applications such as the detection of the coronary border in angiograms, multiple sclerosis lesion quantification, surgery simulations, surgical planning, measuring tumor volume and its response to therapy, functional mapping, automated classification of blood cells, studying brain development, detection of microcalcifications on mammograms, image registration, atlas-matching, heart image extraction from cardiac cineangiograms, detection of tumors, cell high content screening, automatic cancer cell detection, semiconductor wafer inspection, circuit board inspection and alignment etc. Image object segmentation is the basis to follow on object based processing such as measurement, analysis and classification. Therefore, good object segmentation is highly important. If segmented object regions are incorrect. The measurements performed on the segmented objects will certainly be incorrect and therefore any analysis and conclusion drawn based on the incorrect measurements will be erroneous and compromised.
It is difficult to specify what constitutes an object of interest in an image and define the specific segmentation procedures. General segmentation procedures tend to obey the following rules:                Regions of object segmentation should be uniform and homogeneous with respect to some characteristics, such as gray level or texture.        Region interiors should be simple and without many small holes.        Adjacent regions of different objects should have significantly different values with respect to the characteristic on which they are uniform.        Boundaries of each segment should be simple, not ragged, and must be spatially accurate and no overlap.        
However, enforcing the above rules is difficult because strictly uniform and homogeneous regions are typically full of small holes and have ragged boundaries. Insisting that adjacent regions have large differences in values could cause regions to merge and boundaries to be lost. Therefore, it is not possible to create a universal object segmentation method that will work on all types of objects in real life situations.
Prior art segmentation methods are performed in a primitive and ad-hoc fashion on almost all image processing systems. For simple applications, image thresholding is the standard method for object segmentation. This works on images containing bright objects against dark background or dark objects against bright background such as man made parts in machine vision applications. In this case, the object segmentation methods amount to determining a suitable threshold value to separate objects from background (Xiao-Ping Zhang and Mita D. Desai, Wavelet Based Automatic Thresholding for Image Segmentation, In Proc. of ICIP'97, Santa Barbara, Calif., Oct. 26-29, 1997; Sue Wu and Adnan Amin, Automatic Thresholding of Gray-level Using Multi-stage Approach, proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR 2003); Michael H. F. Wilkinson, Tsjipke Wijbenga, Gijs de Vries, and Michel A. Westenberg, BLOOD VESSEL SEGMENTATION USING MOVING-WINDOWROBUST AUTOMATIC THRESHOLD SELECTION, IEEE International Conference on Image Processing, September 2003.). For images with multiple object types with high object boundary contrast, edge detection methods are often used for object segmentation. (Yitzhak Yitzhaky and Eli Peli, A Method for Objective Edge Detection Evaluation and Detector Parameter Selection, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 25, NO. 8, PP. 1027-1033, August 2003.
Application specific object segmentation methods were developed for complicated yet well-defined and high volume applications such as blood cell counting, Pap smear screening, and semiconductor inspection. Human with image processing expertise through extensive programming and trial and error process that involves not only object segmentation module but also optics, illumination, and image acquisition process adjustments developed the application specific object segmentation methods. For complicated yet not well-defined or low volume applications, automatic segmentation method doe not exist. In these applications, object segmentation is often performed by human manually or uses a combination of human and computer interaction.
As an example, prior art cell and tissue segmentation methods are based on simple thresholding followed by rudimentary measurements (Cellomics/ArrayScan, Molecular Devices/Discovery 1, Amersham/IN CELL Analyzer 3000, Atto Biosciences/Pathway HT, Q3DM/EIDAQ 100-HTM). The cell and tissue segmentation results are therefore highly dependent on the ability of the specimen preparation and staining process to create simple, well defined objects of interest that have minimum overlaps. In this case, the cells can be easily segmented by thresholding on simple color or intensity values. They are therefore limited to standard assays and are non-robust and inflexible for changes. This is the state-of-art and the foundation of the current computer cell analysis system.
Cell and tissue high content/context screening assays have the potential to take pivotal role in the drug discovery process in the post-genomic era. High content/context screening assays provide large amounts of biological and chemical information that could help researchers discover the most effective drugs more efficiently, while getting flawed compounds to “fail fast,” thus saving considerable time and expense. Live cell high context screening assays can be used across nearly all stages of the drug discovery and development process, including target identification and validation, lead selection and optimization, and preclinical studies. However, in the live cell assay, in order to maintain the cell nature environment for meaningful studies there is limited control over the staining quality and cell configuration arrangement. The cells could be highly overlapped and live in aggregates. This represents a formidable challenge for fully automatic cell segmentation.
More sophisticated object segmentation methods are disclosed in Brette L. Luck1, Alan C. Bovik1, Rebecca R. Richards-Kortum, SEGMENTING CERVICAL EPITHELIAL NUCLEI FROM CONFOCAL IMAGES USING GAUSSIAN MARKOV RANDOM FIELDS, IEEE International Conference on Image Processing, September 2003”, “Lee, Shih-Jong, U.S. Pat. No. 5,867,610, Method for identifying objects using data processing techniques” and “Lee, Shih-Jong, Oh, Seho, US patent application publication no. 20040202368, Learnable Object Segmentation”, which is incorporated in its entirety herein. However, these more sophisticated object segmentation methods and the thresholding based methods are mostly region based that applies a threshold on some image characteristics. The threshold could be a global one that is either fixed or dynamically determined from the histogram of the image characteristics. The threshold could also be a local one where the values are different for different pixel locations. The underlying assumption of the thresholding approach is that the regions of object should be uniform and homogeneous with respect to the image characteristics of interest. This approach could sufficiently detects significant portions of the object regions. However, the resulting object regions are often not accurate. This is especially the case for the boundaries of the objects. This is because the object region characteristics of interest often are different when close to the boundary of the objects. Therefore, boundaries of an object may be over-segmented or under-segmented by the initial detection methods.
Alternative methods of object segmentation is boundary based method (C. C. Leung, W. F. Chen2, P. C. K. Kwok, and F. H. Y. Chan, Brain Tumor Boundary Detection in MR Image with Generalized Fuzzy Operator, IEEE International Conference on Image Processing, September 2003.) which could yield accurate object boundary but often have gaps between the edges and cannot completely define an object region, problem in object connectivity. The inaccurate object segmentation yields incorrect measurements on the segmented objects (Pascal Bamford, EMPIRICAL COMPARISON OF CELL SEGMENTATION ALGORITHMS USING AN ANNOTATED DATASET, IEEE International Conference on Image Processing, September 2003). Any analysis and conclusion drawn based on the incorrect measurements will be erroneous and compromised.
A region-guided boundary refinement method (Shih-Jong J. Lee, Tuan Phan, “Region-guided Boundary Refinement Method”, U.S. patent application Ser. No. 10/998,282, November 2004) was disclosed to overcome the problem of boundary inaccuracy of the region based segmentation method and the problem of object connectivity in the edge based segmentation method. It uses the initial detection of object regions as the baseline for boundary refinement. This method works only for non-overlapping objects. It fails when the boundary of an object is shared by other objects due to touching or overlapping. Overlapping objects common in many real life applications such as cellular or subcellular object analysis and cell high content screening. This limitation presents a significant disadvantage that hinders quantitative analysis and automation.